def test(cfg: Namespace) -> None:
assert cfg.checkpoint not in [None, ""]
assert cfg.device == "cpu" or (cfg.device == "cuda"
and T.cuda.is_available())
exp_dir = ROOT_EXP_DIR / cfg.exp_name
os.makedirs(exp_dir / "out", exist_ok=True)
cfg.to_file(exp_dir / "test_config.json")
logger.info(f"[exp dir={exp_dir}]")
model = CAE()
model.load_state_dict(T.load(cfg.checkpoint))
model.eval()
if cfg.device == "cuda":
model.cuda()
logger.info(f"[model={cfg.checkpoint}] on {cfg.device}")
dataloader = DataLoader(dataset=ImageFolder720p(cfg.dataset_path),
batch_size=1,
shuffle=cfg.shuffle)
logger.info(f"[dataset={cfg.dataset_path}]")
loss_criterion = nn.MSELoss()
for batch_idx, data in enumerate(dataloader, start=1):
img, patches, _ = data
if cfg.device == "cuda":
patches = patches.cuda()
if batch_idx % cfg.batch_every == 0:
pass
out = T.zeros(6, 10, 3, 128, 128)
avg_loss = 0
for i in range(6):
for j in range(10):
x = patches[:, :, i, j, :, :].cuda()
y = model(x)
out[i, j] = y.data
loss = loss_criterion(y, x)
avg_loss += (1 / 60) * loss.item()
logger.debug("[%5d/%5d] avg_loss: %f", batch_idx, len(dataloader),
avg_loss)
# save output
out = np.transpose(out, (0, 3, 1, 4, 2))
out = np.reshape(out, (768, 1280, 3))
out = np.transpose(out, (2, 0, 1))
y = T.cat((img[0], out), dim=2)
save_imgs(
imgs=y.unsqueeze(0),
to_size=(3, 768, 2 * 1280),
name=exp_dir / f"out/test_{batch_idx}.png",
)
def test(cfg: Namespace) -> None:
assert cfg.checkpoint not in [None, ""]
assert cfg.device == "cpu" or (cfg.device == "cuda"
and T.cuda.is_available())
exp_dir = ROOT_EXP_DIR / cfg.exp_name
os.makedirs(exp_dir / "out", exist_ok=True)
cfg.to_file(exp_dir / "test_config.json")
logger.info(f"[exp dir={exp_dir}]")
model = CAE()
model.load_state_dict(T.load(cfg.checkpoint))
model.eval()
if cfg.device == "cuda":
model.cuda()
logger.info(f"[model={cfg.checkpoint}] on {cfg.device}")
dataloader = DataLoader(dataset=ImageFolder720p(cfg.dataset_path),
batch_size=1,
shuffle=cfg.shuffle)
logger.info(f"[dataset={cfg.dataset_path}]")
loss_criterion = nn.MSELoss()
for batch_idx, data in enumerate(dataloader, start=2):
img, patches, _ = data
print('the patches shape is:', patches.shape)
# print(_)
# plt.imshow(patches[0,:,3,1,:,:].permute(1,2,0))
# # plt.imshow(patches[0].permute(1,2,0))
# plt.show()
if cfg.device == "cuda":
patches = patches.cuda()
if batch_idx % cfg.batch_every == 0:
pass
out = T.zeros(6, 10, 3, 128, 128)
avg_loss = 0
foo = []
for i in range(6):
for j in range(10):
x = patches[:, :, i, j, :, :].cuda()
print('the x shape is:', x.shape)
y = model(x)
print('hellyy', y.shape)
def test(cfg):
os.makedirs(f"./test/{cfg['exp_name']}", exist_ok=True)
model = CAE().cuda()
model.load_state_dict(torch.load(cfg['chkpt']))
model.eval()
logger.info("Loaded model from", cfg['chkpt'])
dataset = ImageFolder720p(cfg['dataset_path'])
dataloader = DataLoader(dataset, batch_size=1, shuffle=cfg['shuffle'])
logger.info(f"Done setup dataloader: {len(dataloader)}")
mse_loss = nn.MSELoss()
for bi, (img, patches, path) in enumerate(dataloader):
out = torch.zeros(6, 10, 3, 128, 128)
# enc = torch.zeros(6, 10, 16, 8, 8)
avg_loss = 0
for i in range(6):
for j in range(10):
x = Variable(patches[:, :, i, j, :, :]).cuda()
y = model(x)
# e = model.enc_x.data
# p = torch.tensor(np.random.permutation(e.reshape(-1, 1)).reshape(1, 16, 8, 8)).cuda()
# out[i, j] = model.decode(p).data
# enc[i, j] = model.enc_x.data
out[i, j] = y.data
loss = mse_loss(y, x)
avg_loss += (1 / 60) * loss.item()
logger.debug('[%5d/%5d] avg_loss: %f' % (bi, len(dataloader), avg_loss))
# save output
out = np.transpose(out, (0, 3, 1, 4, 2))
out = np.reshape(out, (768, 1280, 3))
out = np.transpose(out, (2, 0, 1))
y = torch.cat((img[0], out), dim=2)
save_imgs(imgs=y.unsqueeze(0), to_size=(3, 768, 2 * 1280), name=f"./test/{cfg['exp_name']}/test_{bi}.png")
def imgEncoding(name,patches,checkpoint,interFolder):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
encoder = CAE()
encoder.load_state_dict(torch.load(checkpoint))
encoder.eval()
encoder.to(device)
for idx,patcher in enumerate(patches):
logging.info(f'Doing for {name[idx]}')
out = torch.zeros(6,10, 32, 32, 32)
for i in range(6):
for j in range(10):
x = patcher[None,:, i, j, :, :].to(device)
y = encoder(x)
out[i,j]=y.data
torch.save(out,os.path.join(interFolder,str(name[idx])+'.pt'))
logging.info('Images are encoded')
return encoder
path = '../experiments/training/checkpoint/model_50.pth'
exp_dir = 'output/'
os.makedirs(exp_dir,exist_ok=True)
dataset = custom_single()
dataloader = DataLoader(
dataset=dataset,
batch_size=16,
shuffle=False,
num_workers=4,
)
model = CAE()
model.eval()
state_dict = torch.load(path)
model.load_state_dict(state_dict)
model = model.cuda()
# for batch_idx, data in enumerate(dataloader, start=1):
# patches, _ = data
# patches = patches.float().cuda()
# break
# out = T.zeros(33, 32, 3, 128, 128)
# all_patches = dataset.patches
# all_patches = all_patches.reshape(33,32,3,128,128)
# for i in range(33):
